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Approximate optimal AUTOSAR software components deploying approach for automotive E/E system

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Abstract

The AUTOSAR has been developed as the worldwide standard for automotive E/E software systems, making the electronic components of different suppliers to be employed universally. However, as the number of component-based applications in modern automotive embedded systems grows rapidly and the hardware topology becomes increasingly complex, deploying such large number of components in automotive distributed system in manual way is over-dependent on experience of engineers which in turn is time consuming. Furthermore, the resource limitation and scheduling analysis make the problems more complex for developers to find out an approximate optimal deploying approach in system integration. In this paper, we propose a novel method to deploy the AUTOSAR components onto ECUs with the following features. First, a clustering algorithm is designed for deploying components automatically within relatively low time complexity. Second, a fitness function is designed to balance the ECUs load. The goal of our approach is to minimize the communication cost over all the runnable entities while meeting all corresponding timing constraints and balancing all the ECUs load. The experiment results show that our approach is efficient and has well performance by comparing with other existing methods in specific and synthetic data set.

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References

  • Bhattacharya, A., Konar, A., Das, S., Grosan, C. and Abraham, A. (2008). Hardware software partitioning problem in embedded system design using particle swarm optimization algorithm. Int. Conf. Complex, Intelligent and Software Intensive Systems, 171–176.

    Google Scholar 

  • Billionnet, A., Costa, M. C. and Sutter, A. (1992). An efficient algorithm for a task allocation problem. J. ACM 39, 3, 502–518.

    Article  MathSciNet  MATH  Google Scholar 

  • Chakraverty, S. and Kumar, A. (2007). A rule-based availability-driven cosynthesis scheme. Design Automation for Embedded Systems 11, 2–3, 193–222.

    Article  Google Scholar 

  • Condon, A. and Karp, R. M. (2001). Algorithms for graph partitioning on the planted partition model. Random Structures and Algorithms 18, 2, 116–140.

    Article  MathSciNet  MATH  Google Scholar 

  • Cruz, E. H. M., Diener, M., Pilla, L. L. and Navaux, P. O. A. (2015). An efficient algorithm for communicationbased task mapping. 23rd Euromicro Int. Conf. Parallel, Distributed and Network-Based Processing, 207–214.

    Google Scholar 

  • Dougherty, B., White, J., Balasubramanian, J., Thompson, C. and Schmidt, D. C. (2009). Deployment automation with BLITZ. Int. Conf. Software Engineering, 271–274.

    Google Scholar 

  • Eles, P., Peng, Z., Kuchcinski, K. and Doboli, A. (1997). System level hardware/software partitioning based on simulated annealing and tabu search. Design Automation for Embedded Systems 2, 1, 5–32.

    Article  Google Scholar 

  • Faragardi, H. R., Lisper, B., Sandstrom, K. and Nolte, T. (2014). An efficient scheduling of AUTOSAR runnables to minimize communication cost in multi-core systems. 7th Int. Symp. Telecommunications, 41–48.

    Google Scholar 

  • Ferrandi, F., Lanzi, P. L., Pilato, C. and Sciuto, D. (2010). Ant colony heuristic for mapping and scheduling tasks and communications on heterogeneous embedded systems. IEEE Trans. Computer-Aided Design of Integrated Circuits and Systems 29, 6, 911–924.

    Article  Google Scholar 

  • Ferrari, A., Natale, M. D., Gentile, G., Reggiani, G. and Gai, P. (2009). Time and memory tradeoffs in the implementation of autosar components. Design, Automation and Test in Europe Conference and Exhibition, 864–869.

    Google Scholar 

  • Han, J., Kamber, M. and Pei, J. (2011). Data Mining: Concepts and Techniques. 3rd edn. Morgan Kaufmann. Burlington, Massachusetts, USA.

    MATH  Google Scholar 

  • Han, K. and Cho, J. (2012). Design exploration technique for software component mapping of AUTOSAR development methodology. Lecture Notes in Electrical Engineering 215, 11, 273–281.

    Google Scholar 

  • Hegde, R., Mishra, G. and Gurumurthy, K. S. (2011). An insight into the hardware and software complexity of ECUs in vehicles. Advances in Computing and Information Technology, 99–106.

    Chapter  Google Scholar 

  • IBM CPLEX (2016). http://www-01.ibm.com/software/commerce/optimization/cplex-optimizer

  • Kum, D., Park, G.-M., Lee, S. and Jung, W. (2008). AUTOSAR migration from existing automotive software. Int. Conf. Control, Automation and Systems, 558–562.

    Google Scholar 

  • Liu, C. L. and Layland, J. W. (1973). Scheduling algorithms for multiprogramming in a hard-real-time environment. J. Association for Computing Machinery 20, 1, 46–61.

    Article  MathSciNet  MATH  Google Scholar 

  • Long, R., Li, H., Peng, W. and Zhang, Y. (2009). An approach to optimize intra-ECU communication based on mapping of AUTOSAR runnable entities. Int. Conf. Embedded Software and Systems, 138–143.

    Google Scholar 

  • Niemann, R. and Marwedel, P. (1997). An algorithm for hardware/software partitioning using mixed integer linear programming. Design Automation for Embedded Systems 2, 2, 165–193.

    Article  Google Scholar 

  • Park, I., Chung, J., Youn, J., Lee, W. and Sunwoo, M. (2016). Resource-aware integration of AUTOSARcompliant ECUs with an empirical WCET prediction model. Int. J. Automotive Technology 17, 4, 717–729.

    Article  Google Scholar 

  • Peng, W., Li, H., Yao, M. and Sun, Z. (2010). Deployment optimization for AUTOSAR system configuration. 2nd Int. Conf. Computer Engineering and Technology, 4, V4-189–V4-193.

  • Piao, S., Jo, H., Jin, S. and Jung, W. (2009). Design and implementation of RTE generator for automotive embedded software. 7th ACIS Int. Conf. Software Engineering Research, Management and Applications, 159–165.

    Google Scholar 

  • Rajnak, A. and Kumar, A. (2007). Computer-aided architecture design & optimized implementation of distributed automotive EE systems. 44th ACM/IEEE Design Automation Conf., 556–561.

    Google Scholar 

  • Saidi, S. E., Cotard, S., Chaaban, K. and Marteil, K. (2015). An ILP approach for mapping AUTOSAR runnables on multi-core architectures. Proc. Workshop on Rapid Simulation and Performance Evaluation: Methods and Tools, 6.

    Google Scholar 

  • Vo, G. N., Lai, R. and Garg, M. (2009). Building automotive software component within the AutoSAR environment–A case study. 9th Int. Conf. Quality Software, 191–200.

    Google Scholar 

  • Wozniak, E., Mehiaoui, A., Mraidha, C., Tucci-Piergiovanni, S. and Gerard, S. (2013). An optimization approach for the synthesis of AUTOSAR architectures. IEEE 18th Conf. Emerging Technologies & Factory Automation, 1–10.

    Google Scholar 

  • Wu, J.-G., Srikanthan, T. and Zou, G.-W. (2008). New model and algorithm for hardware/software partitioning. J. Computer Science and Technology 23, 4, 644–651.

    Article  MathSciNet  Google Scholar 

  • Yang, Y. (2012). Software Synthesis for Distributed Embedded Systems. Ph. D. Dissertation. University of California, Berkeley. Berkeley, California, USA.

    Google Scholar 

  • Yoo, J., Lee, J., Park, Y. and Hong, S. (2012). Predicting WCET of automotive software running on virtual machine monitors. Int. J. Automotive Technology 13, 2, 337–346.

    Article  Google Scholar 

  • Yoon, H. and Ryu, M. (2015). Guaranteeing end-to-end deadlines for AUTOSAR-based automotive software. Int. J. Automotive Technology 16, 4, 635–644.

    Article  Google Scholar 

  • Zhang, M. and Gu, Z. (2011). Optimization issues in mapping AUTOSAR components to distributed multithreaded implementations. 22nd IEEE Int. Symp. Rapid System Prototyping, 23–29.

    Google Scholar 

Download references

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Correspondence to Hua Yan.

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Ran, Z., Yan, H., Zhang, H. et al. Approximate optimal AUTOSAR software components deploying approach for automotive E/E system. Int.J Automot. Technol. 18, 1109–1119 (2017). https://doi.org/10.1007/s12239-017-0108-3

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  • DOI: https://doi.org/10.1007/s12239-017-0108-3

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